Abstract
Impact areas in spatial analysis are determined by using density clustering methods which have an elevated computational complexity. We propose an alternative method based on the Extended Fuzzy C-Means (EFCM) and we derive dynamic buffer areas as hypersphere volume prototypes in a GIS environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Di Martino, F., Loia, V., Sessa, S.: Extended Fuzzy C-Means Clustering Algorithm for Hot Spot Events in Spatial Analysis. Internat. Jour. of Hybrid Intelligent System 4, 1–14 (2007)
Kaymak, U., Babuska, R., Setnes, M., Verbruggen, H.B., van Nauta Lemke, H.M.: Methods for Simplification of Fuzzy Models. In: Ruan, D. (ed.) Intelligent Hybrid Systems, pp. 91–108. Kluwer Academic Publishers, Dordrecht (1997)
Kaymak, U., Setnes, M.: Fuzzy Clustering with Volume Prototype and Adaptive Cluster Merging. IEEE Transactions on Fuzzy Systems 10(6), 705–712 (2002)
Murray, A.T., McGuffog, I., Western, J.S., Mullins, P.: Exploratory Spatial Data Analysis Techniques for Examining Urban Crime. British Journal of Criminology 41, 309–329 (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Di Martino, F., Sessa, S. (2008). Dynamic Buffer Areas Obtained by EFCM Method in GIS Environment. In: Sebillo, M., Vitiello, G., Schaefer, G. (eds) Visual Information Systems. Web-Based Visual Information Search and Management. VISUAL 2008. Lecture Notes in Computer Science, vol 5188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85891-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-85891-1_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85890-4
Online ISBN: 978-3-540-85891-1
eBook Packages: Computer ScienceComputer Science (R0)